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dc.contributor.author | Hurtado Oliver, Lluis Felip | es_ES |
dc.contributor.author | González-Barba, José Ángel | es_ES |
dc.contributor.author | Pla Santamaría, Ferran | es_ES |
dc.date.accessioned | 2020-03-27T07:05:18Z | |
dc.date.available | 2020-03-27T07:05:18Z | |
dc.date.issued | 2019-05-14 | es_ES |
dc.identifier.issn | 1064-1246 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/139663 | |
dc.description.abstract | [EN] Natural Language Processing problems has recently been benefited for the advances in Deep Learning. Many of these problems can be addressed as a multi-label classification problem. Usually, the metrics used to evaluate classification models are different from the loss functions used in the learning process. In this paper, we present a strategy to incorporate evaluation metrics in the learning process in order to increase the performance of the classifier according to the measure we are interested to favor. Concretely, we propose soft versions of the Accuracy, micro-F-1, and macro-F-1 measures that can be used as loss functions in the back-propagation algorithm. In order to experimentally validate our approach, we tested our system in an Emotion Classification task proposed at the International Workshop on Semantic Evaluation, SemEval-2018. Using a Convolutional Neural Network trained with the proposed loss functions we obtained significant improvements both for the English and the Spanish corpora. | es_ES |
dc.description.sponsorship | This work has been partially supported by the Spanish MINECO and FEDER founds under project AMIC (TIN2017-85854-C4-2-R) and the GiSPRO project (PROMETEU/2018/176). Work of Jose-Angel Gonzalez is also financed by Universitat Politecnica de Valencia under grant PAID-01-17. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | IOS Press | es_ES |
dc.relation.ispartof | Journal of Intelligent & Fuzzy Systems | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Deep Learning | es_ES |
dc.subject | Loss function | es_ES |
dc.subject | Multi-label classification | es_ES |
dc.subject | Natural Language Processing | es_ES |
dc.subject | Emotion Classification | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | Choosing the right loss function for multi-label Emotion Classification | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3233/JIFS-179019 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/UPV//PAID-01-17/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-85854-C4-2-R/ES/AMIC-UPV: ANALISIS AFECTIVO DE INFORMACION MULTIMEDIA CON COMUNICACION INCLUSIVA Y NATURAL/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//PROMETEO%2F2018%2F176/ES/GISPRO-GENOMIC INFORMATION SYSTEMS PRODUCTION/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació | es_ES |
dc.description.bibliographicCitation | Hurtado Oliver, LF.; González-Barba, JÁ.; Pla Santamaría, F. (2019). Choosing the right loss function for multi-label Emotion Classification. Journal of Intelligent & Fuzzy Systems. 36(5):4697-4708. https://doi.org/10.3233/JIFS-179019 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3233/JIFS-179019 | es_ES |
dc.description.upvformatpinicio | 4697 | es_ES |
dc.description.upvformatpfin | 4708 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 36 | es_ES |
dc.description.issue | 5 | es_ES |
dc.relation.pasarela | S\388529 | es_ES |
dc.contributor.funder | Generalitat Valenciana | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |
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